Joint Decisions in Residential Choice

<p>Economic agents frequently make joint decisions, which often require a compromise by some or all of the participants. We propose an econometric model in which groups of agents make a joint decision; each agent has preferences modelled using a combination of multi-nominal logit and conditional logit parts. We combine these marginal preferences to create a joint set of probabilities of the group making a particular choice, which enables parameter estimation by maximum likelihood. We can also make the weight applied to an individual agents preferences depend on characteristics of the agent or group. To demonstrate the use of the model, data is obtained from the New Zealand Household Travel Survey. We estimate our model to show how households might make the joint decision of where to live, given that different household members have different work locations.</p>

Joint Decisions in Residential Choice

<p>Economic agents frequently make joint decisions, which often require a compromise by some or all of the participants. We propose an econometric model in which groups of agents make a joint decision; each agent has preferences modelled using a combination of multi-nominal logit and conditional logit parts. We combine these marginal preferences to create a joint set of probabilities of the group making a particular choice, which enables parameter estimation by maximum likelihood. We can also make the weight applied to an individual agents preferences depend on characteristics of the agent or group. To demonstrate the use of the model, data is obtained from the New Zealand Household Travel Survey. We estimate our model to show how households might make the joint decision of where to live, given that different household members have different work locations.</p>

A glimpse into the future of exposure and vulnerabilities in cities? Modelling of residential location choice of urban population with random forest

The most common approach to assessing natural hazard risk is investigating the willingness to pay in the presence or absence of such risk. In this work, we propose a new, machine-learning-based, indirect approach to the problem, i.e. through residential-choice modelling. Especially in urban environments, exposure and vulnerability are highly dynamic risk components, both being shaped by a complex and continuous reorganization and redistribution of assets within the urban space, including the (re-)location of urban dwellers. By modelling residential-choice behaviour in the city of Leipzig, Germany, we seek to examine how exposure and vulnerabilities are shaped by the residential-location-choice process. The proposed approach reveals hot spots and cold spots of residential choice for distinct socioeconomic groups exhibiting heterogeneous preferences. We discuss the relationship between observed patterns and disaster risk through the lens of exposure and vulnerability, as well as links to urban planning, and explore how the proposed methodology may contribute to predicting future trends in exposure, vulnerability, and risk through this analytical focus. Avenues for future research include the operational strengthening of these linkages for more effective disaster risk management.

Exploring the dynamics of neighborhood ethnic segregation with agent-based modelling: an empirical application to Bradford

How individuals’ residential moves in space—derived from their varied preferences and constraints—translate into the overall segregation patterns that we observe, remains a key challenge in neighborhood ethnic segregation research. In this paper we use agent-based modeling to explore this concern, focusing on the interactive role of ethnic and socio-economic homophily preferences and housing constraints as determinants of residential choice. Specifically, we extend the notorious Schelling’s model to a random utility discrete choice approach to simulate the relocation decision of people (micro level) and how they translate into spatial segregation outcomes (macro level). We model different weights for preferences of ethnic and socioeconomic similarity in neighborhood composition over random relocations, in addition to housing constraints. We formalize how different combinations of these variables could replicate real segregation scenarios in Bradford, a substantially segregated local authority in the UK. We initialize our model with geo-referenced data from the 2011 Census and use Dissimilarity and the Average Local Simpson Indices as measures of segregation. As in the original Schelling model, the simulation shows that even mild preferences to reside close to co-ethnics can lead to high segregation levels. Nevertheless, ethnic over-segregation decreases, and results come close to real data, when preferences for socioeconomic similarity are slightly above preferences for ethnic similarity, and even more so when housing constraints are considered in relocation moves of agents. We discuss the theoretical and policy contributions of our work.

The Distributional Consequences of Public School Choice

School choice systems aspire to delink residential location and school assignments by allowing children to apply to schools outside of their neighborhood. However, choice programs also affect incentives to live in certain neighborhoods, and this feedback may undermine the goals of choice. We investigate this possibility by developing a model of public school and residential choice. School choice narrows the range between the highest and lowest quality schools compared to neighborhood assignment rules, and these changes in school quality are capitalized into equilibrium housing prices. This compressed distribution generates an ends-against-the-middle trade-off with school choice compared to neighborhood assignment. Paradoxically, even when choice results in improvement in the lowest-performing schools, the lowest type residents need not benefit. (JEL H75, I21, I28, R23, R31)

A glimpse into the future of exposure and vulnerabilities in cities? Modelling of residential location choice of urban population with random forest

Disaster risk is conceived as the interaction of hazard, exposure, and vulnerability. Especially in urban environments, exposure and vulnerability are highly dynamic risk components, both being shaped by a complex and continuous reorganization and redistribution of assets within the urban space, including the residence of urban dwellers. This case study for the city of Leipzig, Germany, proposes an indirect, machine learning-based approach for the prediction of residential choice behaviour to explore how exposure and vulnerabilities are shaped by the residential location choice process. The proposed approach reveals hot spots and cold spots of residential choice for distinct socioeconomic groups exhibiting heterogeneous preferences. We discuss the relationship between observed patterns and disaster risk through the lens of exposure and vulnerability, as well as links to urban planning. Avenues for future research include the operational strengthening of these linkages for more effective disaster risk management.